Abstract

Background

Early phase clinical trials of ICIs in RM-SCCHN have shown promising results, but there is no validated predictive marker of response to date. We hypothesize that baseline host immune recognition creates a distinct microenvironment that is captured in computed tomography (CT)-images. Radiomics uses advanced image processing techniques to extract a large set of quantitative texture and geometric features from tumor regions of interest (ROI), and subject these to a machine learning protocol to train a classifier, which we exploit to develop as a predictive signature of response to ICIs.

Methods

We performed a retrospective analysis of clinical data and CT-images from prospectively enrolled cohorts of RM-SCCHN patients treated with ICIs in our institution. Tumor ROIs were manually contoured from baseline and first on-treatment CT-images. Extracted and computed radiomics features were employed to train a radial basis function support vector machine classifier to discriminate responders from non-responders. Ten-fold cross-validation protocol was employed to determine classifier accuracy.